AI-Powered Infant Growth & Development Monitoring System
A research-driven mobile healthcare platform that leverages Artificial Intelligence to support early childhood development through growth monitoring, Autism risk screening, maternal recovery assessment, and infant discomfort recognition.
TinySteps is an AI-powered mobile healthcare platform developed as our Final Year Research Project for the Bachelor of Science (Hons) in Information Technology specializing in Data Science at the Sri Lanka Institute of Information Technology (SLIIT).
The platform combines Artificial Intelligence, Machine Learning, Explainable AI, and Mobile Health technologies into a unified system that assists caregivers in monitoring infant growth and development while providing intelligent healthcare insights.
- Autism Spectrum Disorder (ASD) Risk Screening
- Infant Growth Monitoring
- Growth Prediction using LSTM
- Maternal Recovery Assessment
- Infant Cry & Discomfort Recognition
- Explainable AI (SHAP & Grad-CAM)
- Personalized Recommendations
- WHO Growth Monitoring
- Clinical Alert Generation
- Daily Health Logging
- Secure Authentication
- REST API Architecture
TinySteps consists of four integrated AI healthcare modules operating through a centralized FastAPI backend.
┌─────────────────────────────┐
│ TinySteps Mobile App │
│ React Native + Expo │
└──────────────┬──────────────┘
│
HTTPS REST API
│
┌──────────────▼──────────────┐
│ FastAPI Backend │
│ Authentication & Business │
│ Logic + AI Service Layer │
└──────────────┬──────────────┘
│
┌─────────────────────────┼────────────────────────┐
│ │ │
▼ ▼ ▼
┌──────────────┐ ┌────────────────┐ ┌────────────────┐
│ Supabase DB │ │ Machine Learning│ │ Storage Layer │
│ PostgreSQL │ │ Model Services │ │ Images / Audio│
└──────┬───────┘ └────────┬───────┘ └────────────────┘
│ │
└──────────────┬───────────┘
│
┌──────────────────────┼─────────────────────────────┐
▼ ▼ ▼
┌───────────────┐ ┌────────────────┐ ┌──────────────────┐
│ ASD Detection │ │ Growth Module │ │ Maternal Recovery│
│ VGGFace │ │ LSTM │ │ Random Forest │
│ XGBoost │ │ Random Forest │ │ Ridge Regression │
│ ExplainableAI │ │ XGBoost │ │ SHAP │
└───────────────┘ └────────────────┘ └──────────────────┘
│
▼
┌────────────────────────┐
│ Infant Cry Recognition │
│ Gradient Boosting │
│ MediaPipe │
│ XGBoost Fusion │
└────────────────────────┘
- Facial Image Analysis
- Q-CHAT Assessment
- Explainable AI
- VGG-Face
- XGBoost
- Monte Carlo Late Fusion
- Daily Growth Logging
- Growth Prediction
- LSTM Forecasting
- WHO WAZ Monitoring
- Random Forest
- XGBoost
- Clinical Rule Engine
- Recovery Assessment
- Personalized Recommendations
- Explainable AI
- Random Forest
- Ridge Regression
- SHAP
- Audio Analysis
- Facial Analysis
- Context-aware Prediction
- Gradient Boosting
- MediaPipe
- XGBoost Fusion
Frontend
- React Native
- Expo
- TypeScript
Backend
- FastAPI
- Python
Database
- Supabase
Machine Learning
- TensorFlow
- Scikit-learn
- XGBoost
- Random Forest
- LSTM
- SHAP
Cloud
- Supabase
TinySteps
│
├── backend/
├── frontend/
├── ml_models/
├── datasets/
├── assets/
│ ├── appICON.png
│ └── screenshots/
├── documentation/
└── README.md
git clone https://github.com/SL-Predetor/infant-growth-monitoring-system.gitBackend
cd backend
pip install -r requirements.txt
uvicorn app.main:app --reloadFrontend
cd frontend
npm install
npx expo starthttp://localhost:8000/docs
| Member | Research Component |
|---|---|
| Yasindu Kularathne | Multi-modal ASD Detection |
| Nethmi Sansala Premachandra | AI Growth Monitoring |
| Dewmi Hennayake | Maternal Recovery Assessment |
| Wikasith Dilshan | Infant Cry & Discomfort Recognition |
Ms. Gaya Thamali Dassanayake
Programme Coordinator
BSc (Hons) Data Science
Sri Lanka Institute of Information Technology
GitHub Repository
https://github.com/SL-Predetor/infant-growth-monitoring-system
Documentation
https://drive.google.com/drive/folders/122_Rl3Q7lMbTo35e0Tf5d9CL34YqQDOi
This project was developed for academic research purposes.
TinySteps
Empowering Caregivers Through Artificial Intelligence